How Chatbots Use Sentiment Analysis to Improve Customer Satisfaction

随着AI在现代客户服务方面的吸引力，虚拟助手正在成为用户体验的一部分。尽管Cortana和Siri之类的旗舰店已成为人类如何与该技术互动的巅峰之作，但像聊天机器人这样的服务使用简单的算法来简化与客户的交流。

聊天机器人are typically used for frontline support, as they relay information about products and services. Customers enjoy chatbots because they provide instantaneous responses and improve the support experience through an efficient, conversational interface.

随着时间的流逝，聊天机器人变得更加复杂，并改编了新的AI功能，以改善客户体验。这些功能之一是情感分析，这允许机器人确定客户信息背后的情感。使用此工具，您将知道客户对话是否与机器人进行得很好。

在这篇文章中，让我们回顾一些情感分析工具，并解释如何使用这些功能来提高您的业务客户满意度。

情感分析功能

在我们深入研究情感分析如何影响客户满意度之前，让我们分解占据这一空间的两个主要工具。

机器学习

机器学习是一种算法，可在对话中改善聊天机器人的性能。当对话触发时，该算法会观察以前的消息并相应地响应。这使对话顺畅地流动，并为客户创造更个性化的感觉。

Natural Language Processing (NLP)

Natural Language Processing, commonly known as NLP, perceives and evaluates customer information. With this tool, scientists can program the chatbot to react differently to messages throughout the interaction. If the bot recognizes negative language, it'll adapt it's responses accordingly or route the conversation to a live agent.

Now that we've covered the tools used for sentiment analysis, let's discuss how this technology has revolutionized chatbots.

聊天机器人如何使用人工智能

Before AI, chatbots were very simple. They could only respond with a few answers and couldn't process any data outside their programmed parameters. As a result, interacting with chatbots was less engaging than speaking with a human rep.

对AI聊天机器人进行了编程，以刺激对话并认识到客户信息背后的基本意图。他们从以前的互动中学习，增强了他们提供相关答案和信息的能力。

情绪分析通过允许机器人解释情绪，使这一能力进一步迈进了一步。让我们回顾以下部分的工作方式。

How Chatbots Use Sentiment Analysis

Once chatbots could communicate effectively, the next step was to improve user experience. After all, it isn't enough to just provide the right answers, you want to create a delightful experience for your customers. With the help of情感分析，聊天机器人可以理解对话是否进展顺利，并相应地回应客户情绪。

使情感分析如此有价值的是它概念化社交互动的能力。想象一下一种算法，可以确定客户对您的产品的看法，为什么他们以这种方式思考以及可以做些什么才能使他们的体验更好。

为了向您展示这在行动中的工作方式，以下是情感分析可以增强用户使用聊天机器人的体验。

1.适应性客户帮助

With sentiment analysis, chatbots can modify their responses so that they're aligned with the customer's emotions. These appropriated responses make for excellent, engaging experiences with customers.

2.路由沮丧或愤怒的客户

Customers who are clearly upset at the start of a conversation are quickly recognized and routed to a live rep. That way, the customer will receive personalized support quickly and efficiently.

3.客户分类

Chatbot data is awesome because it records the entire customer conversation. And, with sentiment analysis, chatbots can identify your happiest and unhappiest users within your客户群。基于客户细分受众satisfaction, you can prioritize support for users at risk of churn and reward customers who have demonstrated long-term loyalty.

4.记录总体客户满意度

除了受众细分外，情感分析还可以识别客户对服务，品牌和产品的整体看法。这为聊天机器人提供了有关客户在与客户互动之前感受的洞察力。

情感分析和其他AI工具将继续在客户服务中流行。采用它们是使您的聊天机器人优势并改善客户的用户体验的好方法。

有关此技术的更多信息，请阅读情感分析tools。